Identification of Visibility Level for Enhanced Road Safety under Different Visibility Conditions: A Hierarchical Clustering-Based Learning Model

Low visibility conditions, particularly those caused by fog, significantly affect road safety and reduce drivers’ ability to see ahead clearly. The conventional approaches used to address this problem primarily rely on instrument-based and fixed-threshold-based theoretical frameworks, which face challenges in adaptability and demonstrate lower performance under varying environmental conditions. To overcome these challenges, we propose a real-time visibility estimation model that leverages roadside CCTV cameras to monitor and identify visibility levels under different weather conditions. The proposed method begins by identifying specific regions of interest (ROI) in the CCTV images and focuses on extracting specific features such as the number of lines and contours detected within these regions. These features are then provided as an input to the proposed hierarchical clustering model, which classifies them into different visibility levels without the need for predefined rules and threshold values. In the proposed approach, we used two different distance similarity metrics, namely dynamic time warping (DTW) and Euclidean distance, alongside the proposed hierarchical clustering model and noted its performance in terms of numerous evaluation measures. The proposed model achieved an average accuracy of 97.81%, precision of 91.31%, recall of 91.25%, and F1-score of 91.27% using the DTW distance metric. We also conducted experiments for other deep learning (DL)-based models used in the literature and compared their performances with the proposed model. The experimental results demonstrate that the proposed model is more adaptable and consistent compared to the methods used in the literature. The proposed method provides drivers real-time and accurate visibility information and enhances road safety during low visibility conditions.